Learning-based resource allocation in wireless networks
File(s)
Author(s)
Blasco Moreno, Pol
Type
Thesis
Abstract
This thesis investigates learning-based resource allocation techniques for future
wireless networks (FWNs). Motivated by recent technological developments,
two types of FWNs are studied: energy harvesting (EH) wireless sensor networks
(WSNs), and high-capacity cellular networks (HC-CNs) with caching capabilities.
In an EH-WSN, each node is powered by a rechargeable battery and harvests
energy from the environment. First, a multi-access throughput optimisation
problem is studied, when the access point schedules EH sensor nodes without
the knowledge of their battery states. A low-complexity policy is shown to be
optimal in certain cases, and a scheduling algorithm, which takes into account
the random processes governing the energy arrivals in the system, is proposed,
and compared to an upper bound. Second, a point-to-point communication
system with an EH transmitter is considered. Since the characteristics of the
environment in which the sensor will be deployed are not known in advance,
we assume no a priori knowledge of the random processes governing the system,
and propose a learning theoretic optimisation for the system operation. The
performance of the proposed algorithm is compared to that of two upper bounds,
obtained by providing more information to the transmitter about the random
processes governing the system.
We then turn our attention to content-level selective offloading to an infostation
terminal in an HC-CN. The infostation, which stores high data-rate content
in its cache memory, allows cellular users in the vicinity to directly download
the stored content through a broadband connection, reducing the latency and
the load on the cellular network. The goal of the infostation cache controller is
to store the most popular content such that the maximum amount of traffic is
o oaded to the infostation. The optimal cache content management problem
when content popularity is unknown is studied, and a number of algorithms to
learn the content popularity pro le are proposed. The performances of these
algorithms are compared to that of an informed upper bound, obtained when
the content popularity pro le is known.
wireless networks (FWNs). Motivated by recent technological developments,
two types of FWNs are studied: energy harvesting (EH) wireless sensor networks
(WSNs), and high-capacity cellular networks (HC-CNs) with caching capabilities.
In an EH-WSN, each node is powered by a rechargeable battery and harvests
energy from the environment. First, a multi-access throughput optimisation
problem is studied, when the access point schedules EH sensor nodes without
the knowledge of their battery states. A low-complexity policy is shown to be
optimal in certain cases, and a scheduling algorithm, which takes into account
the random processes governing the energy arrivals in the system, is proposed,
and compared to an upper bound. Second, a point-to-point communication
system with an EH transmitter is considered. Since the characteristics of the
environment in which the sensor will be deployed are not known in advance,
we assume no a priori knowledge of the random processes governing the system,
and propose a learning theoretic optimisation for the system operation. The
performance of the proposed algorithm is compared to that of two upper bounds,
obtained by providing more information to the transmitter about the random
processes governing the system.
We then turn our attention to content-level selective offloading to an infostation
terminal in an HC-CN. The infostation, which stores high data-rate content
in its cache memory, allows cellular users in the vicinity to directly download
the stored content through a broadband connection, reducing the latency and
the load on the cellular network. The goal of the infostation cache controller is
to store the most popular content such that the maximum amount of traffic is
o oaded to the infostation. The optimal cache content management problem
when content popularity is unknown is studied, and a number of algorithms to
learn the content popularity pro le are proposed. The performances of these
algorithms are compared to that of an informed upper bound, obtained when
the content popularity pro le is known.
Version
Open Access
Date Issued
2014-08
Date Awarded
2014-11
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
Advisor
Gündüz, Deniz
Publisher Department
Electrical and Electronic Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)